Tax Increment Financing districts are a funding tool to “build and repair roads and infrastructure, clean polluted land and put vacant properties back to productive use, usually in conjunction with private development projects”.
Funds are generated by the increase in property value properties in the designated areas. The property taxes generated off of any growth after the area designation are then used t fund projects. In other words, it comes only from the difference in property value after designation.
The areas themselves must be areas characterized by blight which means areas that potentially have excessive vacancies, lack of physical maintenance, lack of community planning, dilapidation, etc. That is how they supposed to work.
The census data used includes income data, mean commute time in minutes and property value. All census data is aggregated by tract, which was the basis of all joins used for the final processed input data. CMAP data used includes metrics on transit Accessibility, household density and employment accessibility. Finally, from the Chicago Data Portal, the boundaries for TIFs and TIF expenditures were obtained. All of this data was combined using spatial joins and joins by census tract ID wherever possible.
When we look at the transit accessibility versus propoerty value, we find a light correlation between indicating that those areas with higher property values tend to have more access to transit means, and to some degree, more employment.
## household_incomeE emp_mile_med prop_value_medE HH_per_acr_med
## household_incomeE 1.000000000 0.60083502 0.56145497 0.41874882
## emp_mile_med 0.600835020 1.00000000 0.28332164 0.32925726
## prop_value_medE 0.561454968 0.28332164 1.00000000 0.45070259
## HH_per_acr_med 0.418748819 0.32925726 0.45070259 1.00000000
## walk_score_med 0.286211447 0.28588779 0.49809493 0.59034926
## trans_avail_med 0.001746722 0.13244741 0.18860144 0.42747352
## tif_expend_med -0.024268324 0.03423361 -0.04756442 0.01228008
## walk_score_med trans_avail_med tif_expend_med
## household_incomeE 0.28621145 0.001746722 -0.02426832
## emp_mile_med 0.28588779 0.132447413 0.03423361
## prop_value_medE 0.49809493 0.188601442 -0.04756442
## HH_per_acr_med 0.59034926 0.427473520 0.01228008
## walk_score_med 1.00000000 0.703326418 0.08768285
## trans_avail_med 0.70332642 1.000000000 0.11132661
## tif_expend_med 0.08768285 0.111326613 1.00000000
We see some similar correlations with property value, walkability and TIF expenditure.
| km_label_2 | hh_income | prop_value | transit_avail | walkability |
|---|---|---|---|---|
| Low Development | 75956100 | 185600 | 4.75 | 71.5 |
| Medium Development | 154461500 | 357700 | 5.00 | 107.0 |
| High Development | 617522900 | 410400 | 5.00 | 132.5 |
## # A tibble: 3 Ă— 5
## kkm_label hh_income prop_value transit_avail walkability
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 High Development 61574000 179900 4.88 72.5
## 2 Medium Development 149208000 250300 4.5 82.5
## 3 Low Development 177349000 428600 5 119.
## # A tibble: 4 Ă— 5
## db_label hh_income prop_value transit_avail walkability
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Low Development 93886600 220000 5 82.8
## 2 Medium Development 182322500 159250 4.89 80
## 3 High Development 301013800 393900 4.12 75
## 4 Excluded 397491400 413050 5 122